What does Leidos do? Part 9: Data Science and Engineering

What does Leidos do? The final interview of our series explores data science and engineering with David Keever, Leidos Senior Program Manager and Technical Fellow, who leads the technology area.

Here’s what we learned.

Q: What is data science and engineering, and how is Leidos involved?

Data is what I consider the 21st century equivalent of 20th century oil. In the beginning of the industrial age, oil was the key commodity. It was the enabler for transportation, industrial production, and personal wealth. Now data is that very prominent and important commodity. We collect it, analyze it, and turn it into vital products and services. The data isn’t going away. There’s just going to be more and more, and it’s going to be used in novel ways that many people can’t even imagine now.

This data revolution spans government, non-profit, and commercial markets alike. Our customers welcome usable insights in analyzing their data to understand trends, help make predictions, fulfill mission needs or commercial objectives, increase personal safety, and bolster national security. The Leidos role is to work with our customers to find out how to effectively use their data sets for the full life-cycle.

David Keever, Data Science and Engineering Lead —
"The data isn't going away. There's just going to be more and more, and it's going to be used in novel ways that many people can't even imagine now."

What’s an example of data presenting opportunities for our customers?

In the defense area, particularly in logistics, there are thousands upon millions of moving parts that support national security endeavors, whether it’s the military or other agencies. Understanding secure supply chains, how to track and optimize the movement of assets — it’s all very data intensive. The global supply chain has many suppliers who communicate in different ways. Understanding the flow of order creation, inventory on-hand, how to be cost effective and still meet mission needs — it’s a very complicated problem. There’s an enormous opportunity to make sense of the data to become more efficient.

How did Leidos make a name for itself in this area?

Sea Hunter is a good example, which is the maritime application of autonomy. Autonomous vehicles have a variety of sensors for navigation purposes. The Sea Hunter program, which evolved out of a DARPA initiative, involved helping a 130-foot-long vessel navigate without a human being at the helm. Last fall, Leidos demonstrated this capability by having the vessel navigate Tampa Bay. It successfully navigated the bay, missed all the buoys and boats, and ultimately got to its destination with a flawless performance.

You can imagine all the sensors analyzing real-time data: Where am I? Where’s the sun? What’s the weather? How big are the waves? Are there boats around me? Are there buoys? Is something coming at me quickly? Am I following the rules of navigation properly? All these calculations were running simultaneously on computers aboard the vessel without the aid of humans. It’s extremely data intensive. We’ve done it in a maritime application, and we’re getting inquiries from companies doing it for land-based transportation.

What cutting-edge data science technology is Leidos working on?

Predictive analysis is an important one. Forecasting the future is very challenging. You can always look at the past and conclude that’s the way the future will be over the next 30 minutes, hours, days, whatever. But as you forecast further into the future, the chances of error become greater and greater. There’s no perfect predictability of the future.

Well, how does data help? If you have data from multiple sources, you’re able to make more accurate long-term predictions. These prediction can translate into real money and security implications. For example, retailers have to get their goods distributed from ports to the center of the country by rail, and time is money. There are predictive models that help fine-tune logistics and allow for safe cargo passage. This predictive element fans out from one or two single events like a ship coming into port. This event has many cascading effects, and data analytics can help understand how that event affects thousands of people in other parts of the country.

In healthcare, we’re developing techniques that use data to make sure patients get the best possible care. One very common surgical procedure in the U.S. is knee replacement, which can involve a very complicated set of activities from initial diagnosis to surgery preparation, lab work, postoperative care, physical therapy, and so on. Hundreds of little decisions, and all kinds of data that has to come together in a very coherent and timely manner, all while protecting a patient’s private information. Leidos helps make sense of the data that’s needed to make these processes very efficient and coordinated across multiple stakeholders, from patients to hospitals, medical professionals, insurers, and suppliers.

What are some interesting niche areas Leidos is involved with?

We support a lot of data-driven startups. We play very well in that particular space, because we’ve got a very strong science, engineering, and technology base. We’re pulling together data from certain types of sensors, and as an example, we think we have a very viable, cost effective way of allowing for 3D night vision. It’s still a research project, but you can imagine that this would be valuable for personal security, national security and law enforcement.

There are many niche areas that deal with particular uses of social media data. We help with digital reputation for certain large commercial firms. We’re able to monitor and make sure customer service issues don’t go unaddressed. There’s really an endless amount of services and service analytics that can be done with data.

What gives Leidos an advantage over its competitors in this domain?

With 60 percent of our employees at customer sites, we tend to be very customer focused. We listen to their opportunities and concerns and think about how we can help solve the problem or add value. We don’t tend to come in with a canned solution. I’ve seen too many examples when someone will come in with a machine learning model or set of data, usually with a graphically appealing user interface and try to force fit it to a customer issue or concern. It just doesn’t work. We listen in order to understand the real pain point. Those pain points can be symptomatic, so we’re trying to find the real underlying problem, which ultimately leads you to data and the proper uses of data.